SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Incipience, growth and detachment of boiling bubbles in saturated distilled water from artificial nucleation sites of given geometry and size in heated metal surface
Temperature induced surface tension variation effect on motion of bubbles and drops, noting surface active substances
Flow visualization technique study of wake existence behind large air bubble rising through quiescent liquid
Time dependent pressure distribution and threshold acceleration for bubble formation in longitudinally vibrating flexible liquid filled cylinder
A two-frequency acoustic apparatus has been developed to study the dynamics of a single gas or vapor bubble in water.
A two-frequency acoustic apparatus is used to study resonant coupling of oscillating gas or vapor bubbles in water.
Two ultrasonic devices for the positioning and the remote manipulation of free drops and bubbles have been used during the recent STS-94 Spacelab flight.
Large bubbles are levitated in a primary acoustic field, trapped slightly above the pressure nodes.
The North Galactic Pole Rift (NGPR) is one of the few distinct neutral hydrogen clouds at high Galactic latitudes that have well-defined distances. It is located at the edge of the Local Cavity (LC) and provides an important test case for understanding the Local Hot Bubble (LHB), the presumed location for the hot diffuse plasma responsible for much of the observed 1/4 keV emission originating in the solar neighborhood. Using data from the ROSAT All- Sky Survey and the Planck reddening map, we find the path length within the LC (LHB plus Complex of Local Interstellar Clouds) to be 98 plus or minus 27 pc, in excellent agreement with the distance to the NGPR of 98 +/- 6 pc. In addition, we examine another 14 directions that are distributed over the sky where the LC wall is apparently optically thick at 1/4 keV. We find that the data in these directions are also consistent with the LHB model and a uniform emissivity plasma filling most of the LC.
Diffuse X-rays from the Local Galaxy (DXL) is a sounding rocket mission designed to quantify and characterize the contribution of Solar Wind Charge eXchange (SWCX) to the Diffuse X-ray Background and study the properties of the Local Hot Bubble (LHB). Based on the results from the DXL mission, we quantified and removed the contribution of SWCX to the diffuse X-ray background measured by the ROSAT All Sky Survey. The cleaned maps were used to investigate the physical properties of the LHB. Assuming thermal ionization equilibrium, we measured a highly uniform temperature distributed around kT = 0.097 keV +/- 0.013 keV (FWHM) +/- 0.006 keV(systematic). We also generated a thermal emission measure map and used it to characterize the three-dimensional (3D) structure of the LHB, which we found to be in good agreement with the structure of the local cavity measured from dust and gas.
Large amplitude oscillations of drops and bubbles immersed in an immiscible liquid host have been investigated using ultrasonic radiation pressure techniques.
The complex drivers of equatorial plasma bubbles and resulting scintillation requires a system science approach spanning the M-ITM disciplines. The current roadmap missions strongly support this approach, but gaps are identified in planned observations, with potential mission and solutions proposed.
To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.
Presentation on simulations of U3Si2 fission gas bubble interconnection for TMS 2020 conference.
A precise determination of the bubble wall velocity v$_{w}$ is crucial for making accurate predictions of the baryon asymmetry and gravitational wave (GW) signals in models of electroweak baryogenesis (EWBG). Working in the local thermal equilibrium approximation, we exploit entropy conservation to present efficient algorithms for computing v$_{w}$, significantly streamlining the calculation. We then explore the parameter dependencies of v$_{w}$, focusing on two sample models capable of enabling a strong first-order electroweak phase transition: a ℤ$_{2}$-symmetric singlet extension of the SM, and a model for baryogenesis with CP violation in the dark sector. We study correlations among v$_{w}$ and the two common measures of phase transition strength, α$_{n}$ and v$_{n}$/T$_{n}$. Interestingly, we find a relatively model-insensitive relationship between v$_{n}$/T$_{n}$ and α$_{n}$. We also observe an upper bound on α$_{n}$ for the deflagration/hybrid wall profiles naturally compatible with EWBG, the exact value for which varies between models, significantly impacting the strength of the GW signals. In summary, our work provides a framework for exploring the feasibility of EWBG models in light of future GW signals.
Molten chloride salt fast reactors (MCFRs) will require UCl 3 dissolved in molten salt mixtures as fuel for nuclear fission. For infusing the salt with UCl 3 , bubbling HCl into NaCl-CaCl 2 in contact with U metal was investigated. The reaction was run up to 9 h and yielded U concentration up to 0.652 wt.%. Open circuit potential between a W electrode and Ag/AgCl reference electrode yielded a potential consistent with uranium existing as U(III) in the salt. Furthermore, this demonstrates that HCl can be a very effective chlorinating agent to infuse MCFR fuel with UCl 3 starting from U metal.
In this paper, we developed a machine learning-based Bayesian approach to inversely quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) simulations for bubbly flows. The proposed approach is supported by high-resolution two-phase flow measurements, including those by double-sensor conductivity probes, high-speed imaging, and particle image velocimetry. Local distributions of key physical quantities of interest (QoIs), including the void fraction and phasic velocities, are obtained to support the Bayesian inference. In the process, the epistemic uncertainties of the closure relations are inversely quantified while the aleatory uncertainties from stochastic fluctuations of the system are evaluated based on experimental uncertainty analysis. The combined uncertainties are then propagated through the MCFD solver to obtain uncertainties of the QoIs, based on which probability-boxes are constructed for validation. The proposed approach relies on three machine learning methods: feedforward neural networks and principal component analysis for surrogate modeling, and Gaussian processes for model form uncertainty modeling. The whole process is implemented within the framework of an open-source deep learning library PyTorch with graphics processing unit (GPU) acceleration, thus ensuring the efficiency of the computation. The results demonstrate that with the support of high-resolution data, the uncertainties of MCFD simulations can be significantly reduced. The proposed approach has the potential for other applications that involve numerical models with empirical parameters.
Although coveted in applications, few materials expand when subject to compression or contract under decompression, i.e., exhibit negative compressibility. A key step to achieve such counterintuitive behaviour is the destabilisations of (meta)stable equilibria of the constituents. Here, we propose a simple strategy to obtain negative compressibility exploiting capillary forces both to precompress the elastic material and to release such precompression by a threshold phenomenon – the reversible formation of a bubble in a hydrophobic flexible cavity. We demonstrate that the solid part of such metastable elastocapillary systems displays negative compressibility across different scales: hydrophobic microporous materials, proteins, and millimetre-sized laminae. This concept is applicable to fields such as porous materials, biomolecules, sensors and may be easily extended to create unexpected material susceptibilities.